






"""simple docstring"""







def _a					(						_snake_case ,			_snake_case				):







      """simple docstring"""






      UpperCAmelCase							     =			word.split()

      def justify(_snake_case ,			_snake_case ,			_snake_case				) -> str:
            UpperCAmelCase							     =			max_width - width
            UpperCAmelCase							     =			len(_snake_case				)
            if len(_snake_case				) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  UpperCAmelCase							     =			words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  UpperCAmelCase							     =			spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  UpperCAmelCase							     =			(
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(_snake_case				):
                        num_spaces_between_words_list[i] += 1
                  UpperCAmelCase							     =			[]
                  for i in range(_snake_case				):
                        # add the word
                        aligned_words_list.append(line[i]				)
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * """ """				)
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]				)
                  # join the aligned words list to form a justified line
                  return "".join(_snake_case				)

      UpperCAmelCase							     =			[]
      UpperCAmelCase							     =			[]
      UpperCAmelCase							     =			0
      for word in words:
            if width + len(_snake_case				) + len(_snake_case				) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(_snake_case				)
                  width += len(_snake_case				)
            else:
                  # justify the line and add it to result
                  answer.append(justify(_snake_case ,			_snake_case ,			_snake_case				)				)
                  # reset new line and new width
                  UpperCAmelCase     ,   UpperCAmelCase							     =			[word], len(_snake_case				)
      UpperCAmelCase							     =			max_width - width - len(_snake_case				)
      answer.append(""" """.join(_snake_case				) + (remaining_spaces + 1) * """ """				)
      return answer


if __name__ == "__main__":
  from doctest import testmod

  testmod()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a    :	Tuple         =    {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Dict         =    [
		    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LukeForEntityClassification""",
		    """LukeForEntityPairClassification""",
		    """LukeForEntitySpanClassification""",
		    """LukeForMultipleChoice""",
		    """LukeForQuestionAnswering""",
		    """LukeForSequenceClassification""",
		    """LukeForTokenClassification""",
		    """LukeForMaskedLM""",
		    """LukeModel""",
		    """LukePreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		__a    :	Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)